Evaluating the influential priority of the factors on insurance loss of public transit.

Wenhui Zhang,Xinqiang Chen,Yongmin Su,Ruimin Ke,Xiaolei Ma

doi:10.1371/journal.pone.0190103

Wenhui Zhang, Xinqiang Chen + Show 3 more

Open Access

https://doi.org/10.1371/journal.pone.0190103

Copy DOI

Abstract

Understanding correlation between influential factors and insurance losses is beneficial for insurers to accurately price and modify the bonus-malus system. Although there have been a certain number of achievements in insurance losses and claims modeling, limited efforts focus on exploring the relative role of accidents characteristics in insurance losses. The primary objective of this study is to evaluate the influential priority of transit accidents attributes, such as the time, location and type of accidents. Based on the dataset from Washington State Transit Insurance Pool (WSTIP) in USA, we implement several key algorithms to achieve the objectives. First, K-means algorithm contributes to cluster the insurance loss data into 6 intervals; second, Grey Relational Analysis (GCA) model is applied to calculate grey relational grades of the influential factors in each interval; in addition, we implement Naive Bayes model to compute the posterior probability of factors values falling in each interval. The results show that the time, location and type of accidents significantly influence the insurance loss in the first five intervals, but their grey relational grades show no significantly difference. In the last interval which represents the highest insurance loss, the grey relational grade of the time is significant higher than that of the location and type of accidents. For each value of the time and location, the insurance loss most likely falls in the first and second intervals which refers to the lower loss. However, for accidents between buses and non-motorized road users, the probability of insurance loss falling in the interval 6 tends to be highest.

Highlights

The purpose of vehicle insurances is to cover the claims of policyholders from accidents
We firstly explored the influential priority of factors by use of GRA; applied Naïve Bayes theory to calculate the probabilities of factor values to insurance loss
We used K-means algorithm to cluster the insurance loss data, GRA was applied to calculate the influential priority of three factors, Bayes theory was applied to explore the probability of factor values to each interval of loss

Summary

Introduction

The purpose of vehicle insurances is to cover the claims of policyholders from accidents. Vehicles without insurance are forbidden to run on public roads in many countries. This kind of compulsory regulation ensures reasonable financial compensations for losses of the third parties involved in accidents. Besides the compulsory third-party liability coverage, most insurers offer the first-party coverage aiming to provide compensation for the insured party (vehicle damages and personal injuries). Most insurers use the performance of vehicles, claim counts and previous insurance losses to calculate the future premium [1,2,3,4].

Objectives

Methods

Results

Conclusion